A Hybrid and Adaptive Learning Framework for Personalized Restaurant Recommendations

This dissertation explores the development and implementation of an advanced restaurant recommendation system, leveraging state-of-the-art machine learning and artificial intelligence techniques. The research aims to enhance the dining experience by delivering personalized dish recommendations across multiple restaurants. The proposed system integrates a variety of computational approaches, including hybrid recommendation methods, adaptive learning strategies, and sequential modeling, to provide tailored and dynamic suggestions. By considering a broad spectrum of factors such as user preferences, contextual information, and cross-restaurant data, the system aims to deliver highly accurate and relevant recommendations. Key challenges in recommendation systems, such as cold-start problems and the balance between exploration and exploitation, are addressed through innovative solutions. The system’s performance is evaluated using both offline and online metrics, offering a comprehensive assessment of its effectiveness. This research contributes to the fields of recommendation systems and restaurant technology, providing insights that have the potential to transform how customers engage with restaurant menus and how establishments optimize their offerings to improve customer satisfaction and business outcomes.